current TeX

tex/chapters/work.tex

@@ -35,6 +35,7 @@
 	 
 
 	\subsection{Signal Strength Prediction Model}
+	\label{sec:sigStrengthModel}
 		
 		\begin{equation}
 			\mRssi = \mTXP{} + 10 \mPLE{} + \log_{10} \frac{d}{d_0} + \mGaussNoise{}
@@ -97,6 +98,16 @@
 		
 	\subsection{Model Parameter Optimization}
 	
+		\begin{figure}[t!]
+			\input{gfx/wifiop_show_optfunc_params}
+			\label{fig:wifiOptFuncTXPEXP}
+			\caption{
+				The average error (in \SI{}{\decibel}) between all reference measurements and corresponding model predictions
+				for one \docAPshort{} dependent on \docTXP{} \mTXP{} and \docEXP{} \mPLE{}
+				[known position $\mPosAPVec{}$, fixed \mWAF{}] denotes a convex function.
+			}
+		\end{figure}
+	
 		For systems that demand a higher accuracy, one can choose a compromise between fingerprinting and
 		pure empiric model parameters where (some) model parameters are optimized,
 		based on a few reference measurements throughout the building. 
@@ -115,15 +126,7 @@
 			TODO TODO TODO
 		\end{equation}
 		
-		\begin{figure}
-			\input{gfx/wifiop_show_optfunc_params}
-			\label{fig:wifiOptFuncTXPEXP}
-			\caption{
-				The average error (in \SI{}{\decibel}) between all reference measurements and corresponding model predictions
-				for one \docAPshort{} dependent on \docTXP{} \mTXP{} and \docEXP{} \mPLE{}
-				[known position $\mPosAPVec{}$, fixed \mWAF{}] denotes a convex function.
-			}
-		\end{figure}
+		
 		
 		However, optimizing an unknown transmitter position usually means optimizing a non-convex, discontinuous
 		function, especially when the $z$-coordinate, that influences the number of attenuating floors/ceilings,
@@ -133,7 +136,7 @@
 		As can be seen in figure \ref{fig:wifiOptFuncPosYZ}, there are two local minima and only one of
 		both also is a global one.
 	
-		\begin{figure}
+		\begin{figure}[t!]
 			\input{gfx/wifiop_show_optfunc_pos_yz}
 			\label{fig:wifiOptFuncPosYZ}
 			\caption{